Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	141,378
2	Rhode Island	139,882
3	South Dakota	138,652
4	Utah	124,167
5	Tennessee	121,967
6	Arizona	118,692
7	Iowa	115,851
8	Nebraska	113,795
9	Wisconsin	113,679
10	Oklahoma	113,294
11	South Carolina	112,702
12	New Jersey	112,585
13	Arkansas	111,328
14	Indiana	107,834
15	Alabama	107,806
16	Delaware	107,735
17	Kansas	106,826
18	Illinois	106,004
19	New York	105,534
20	Idaho	105,048
21	Mississippi	104,799
22	Florida	104,423
23	Minnesota	102,724
24	Nevada	102,409
25	Montana	102,084
26	Georgia	101,561
27	Wyoming	100,459
28	Kentucky	100,351
29	Massachusetts	100,278
30	Texas	99,918
31	Louisiana	98,645
32	Missouri	98,150
33	Connecticut	95,148
34	California	94,781
35	New Mexico	94,300
36	Michigan	94,062
37	North Carolina	93,151
38	Alaska	92,404
39	Ohio	91,966
40	Pennsylvania	90,703
41	Colorado	89,661
42	West Virginia	85,884
43	Virginia	77,477
44	Maryland	74,297
45	New Hampshire	70,145
46	District of Columbia	67,807
47	Washington	53,403
48	Puerto Rico	52,037
49	Maine	46,009
50	Oregon	44,181
51	Vermont	37,061
52	Hawaii	23,299

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Minnesota	313
2	Delaware	298
3	Colorado	256
4	Michigan	247
5	Pennsylvania	244
6	Florida	226
7	Maine	223
8	West Virginia	220
9	Illinois	202
10	Puerto Rico	199
11	Oregon	198
12	Indiana	183
13	New York	179
14	North Dakota	179
15	South Carolina	176
16	New Jersey	174
17	New Hampshire	171
18	Massachusetts	170
19	Vermont	161
20	Maryland	139
21	Washington	134
22	Georgia	123
23	Missouri	122
24	Ohio	120
25	Wisconsin	118
26	Kentucky	116
27	Virginia	116
28	District of Columbia	113
29	Iowa	110
30	Utah	106
31	Arizona	104
32	Montana	103
33	Nebraska	97
34	Hawaii	96
35	Rhode Island	90
36	North Carolina	76
37	Texas	76
38	Alabama	73
39	Connecticut	73
40	Kansas	64
41	Alaska	63
42	Nevada	55
43	Tennessee	50
44	Louisiana	49
45	Arkansas	48
46	South Dakota	48
47	California	45
48	New Mexico	45
49	Wyoming	42
50	Oklahoma	31
51	Idaho	27
52	Mississippi	27

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,882
2	New York	2,664
3	Massachusetts	2,556
4	Rhode Island	2,521
5	Mississippi	2,418
6	Arizona	2,382
7	Connecticut	2,271
8	Louisiana	2,233
9	Alabama	2,225
10	South Dakota	2,223
11	Pennsylvania	2,056
12	North Dakota	2,001
13	Indiana	1,982
14	New Mexico	1,940
15	Illinois	1,922
16	Arkansas	1,902
17	Michigan	1,890
18	Iowa	1,888
19	South Carolina	1,849
20	Georgia	1,844
21	Nevada	1,773
22	Tennessee	1,771
23	Texas	1,740
24	Kansas	1,724
25	Oklahoma	1,715
26	Delaware	1,669
27	Ohio	1,649
28	Florida	1,642
29	District of Columbia	1,567
30	California	1,566
31	Missouri	1,507
32	West Virginia	1,498
33	Kentucky	1,484
34	Montana	1,472
35	Maryland	1,451
36	Wisconsin	1,299
37	Minnesota	1,286
38	Virginia	1,264
39	Nebraska	1,223
40	Wyoming	1,221
41	North Carolina	1,208
42	Idaho	1,146
43	Colorado	1,117
44	New Hampshire	959
45	Washington	728
46	Puerto Rico	724
47	Utah	687
48	Oregon	596
49	Maine	586
50	Alaska	451
51	Vermont	395
52	Hawaii	339

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Michigan	5
2	Maryland	3
3	California	2
4	Florida	2
5	Illinois	2
6	Iowa	2
7	New Jersey	2
8	New York	2
9	Ohio	2
10	Pennsylvania	2
11	West Virginia	2
12	Alabama	1
13	Arizona	1
14	Connecticut	1
15	Georgia	1
16	Indiana	1
17	Kentucky	1
18	Maine	1
19	Massachusetts	1
20	Minnesota	1
21	Montana	1
22	New Mexico	1
23	North Dakota	1
24	Puerto Rico	1
25	South Carolina	1
26	South Dakota	1
27	Texas	1
28	Virginia	1
29	Wisconsin	1
30	Alaska	0
31	Arkansas	0
32	Colorado	0
33	Delaware	0
34	District of Columbia	0
35	Hawaii	0
36	Idaho	0
37	Kansas	0
38	Louisiana	0
39	Mississippi	0
40	Missouri	0
41	Nebraska	0
42	Nevada	0
43	New Hampshire	0
44	North Carolina	0
45	Oklahoma	0
46	Oregon	0
47	Rhode Island	0
48	Tennessee	0
49	Utah	0
50	Vermont	0
51	Washington	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Chattahoochee	Georgia	361,511	1	99
Crowley	Colorado	359,842	2	99
Bent	Colorado	273,265	3	99
Dewey	South Dakota	248,133	4	99
Lincoln	Arkansas	245,700	5	99
Davidson	Tennessee	141,445	186	94
Richland	South Carolina	110,913	985	68
York	South Carolina	109,524	1040	66
Orange	California	85,110	2186	30
Pierce	Washington	54,479	2874	8

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Galax city	Virginia	8,035	2	99
Jerauld	South Dakota	7,948	3	99
Emporia city	Virginia	7,856	4	99
Hancock	Georgia	7,686	5	99
Orange	California	1,565	1821	42
York	South Carolina	1,342	2079	33
Davidson	Tennessee	1,341	2081	33
Richland	South Carolina	1,313	2112	32
Pierce	Washington	717	2742	12

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.